Giotto Suite

Giotto Suite is a major upgrade to the Giotto package that provides tools to process, analyze and visualize spatial multi-omics data at all scales and multiple resolutions. The underlying framework is generalizable to virtually all current and emerging spatial technologies. Our Giotto Suite prototype pipeline is generally applicable on various different datasets, such as those created by state-of-the-art spatial technologies, including in situ hybridization (seqFISH+, merFISH, osmFISH, CosMx), sequencing (Slide-seq, Visium, STARmap, Seq-Scope, Stereo-Seq) and imaging-based multiplexing/proteomics (CyCIF, MIBI, CODEX). These technologies differ in terms of resolution (subcellular, single cell or multiple cells), spatial dimension (2D vs 3D), molecular modality (protein, RNA, DNA, …), and throughput (number of cells and analytes).

Installation

To install Giotto suite use pak::pkg_install("drieslab/Giotto").

Visit the Giotto Discussions page for more information.

Website Update!

With Giotto version 4.0, we updated the website at http://giottosuite.com, you can still find the previous website at https://giottosuite.readthedocs.io/en/latest/

Website description

• Get started: Here you can find more advanced information about the Giotto object, Giotto ecosystem, Giotto configuration, and installation FAQs.
• Documentation: Here you will find all Giotto functions grouped by their purpose (Helpers, Getters & Setters, Visualization, ...)
• Examples: Here you can find end-to-end examples for different technologies and datasets.
• Tutorials: Here you can find various tutorials on working with Giotto (analysis, visualizations, working on the cloud, ...)
• News: Here you can find the changelog for every Giotto release and video recordings from previous presentations.

References

• Jiaji George Chen, Joselyn Cristina Chávez-Fuentes, et al. Giotto Suite: a multi-scale and technology-agnostic spatial multi-omics analysis ecosystem. bioRxiv (2023).
• Dries, R., Zhu, Q. et al. Giotto: a toolbox for integrative analysis and visualization of spatial expression data. Genome Biology (2021).
• Dries, R., Chen, J. et al. Advances in spatial transcriptomic data analysis. Genome Research (2021).
• Del Rossi, N., Chen, J. et al. Analyzing Spatial Transcriptomics Data Using Giotto. Current Protocols (2022).